Fix numerical problems in EMGLLF (R version)

diff --git a/pkg/R/EMGLLF.R b/pkg/R/EMGLLF.R
index b71a128..6ee7ba7 100644 (file)
--- a/pkg/R/EMGLLF.R
+++ b/pkg/R/EMGLLF.R
@@ -23,7 +23,7 @@
 #'
 #' @export
 EMGLLF <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda, 
-  X, Y, eps, fast = TRUE)
+  X, Y, eps, fast)
 {
   if (!fast)
   {
@@ -111,7 +111,8 @@ EMGLLF <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda,
 
     # t(m) is the largest value in the grid O.1^k such that it is nonincreasing
     while (kk < 1000 && -a/n + lambda * sum(pi^gamma * b) <
-      -sum(gam2 * log(pi2))/n + lambda * sum(pi2^gamma * b))
+      # na.rm=TRUE to handle 0*log(0)
+      -sum(gam2 * log(pi2), na.rm=TRUE)/n + lambda * sum(pi2^gamma * b))
     {
       pi2 <- pi + 0.1^kk * (1/n * gam2 - pi)
       kk <- kk + 1
@@ -138,8 +139,8 @@ EMGLLF <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda,
       {
         for (mm in 1:m)
         {
-          S[j, mm, r] <- -rho[mm, mm, r] * ps2[j, mm, r]
-            + sum(phi[-j, mm, r] * Gram2[j, -j, r])
+          S[j, mm, r] <- -rho[mm, mm, r] * ps2[j, mm, r] +
+            sum(phi[-j, mm, r] * Gram2[j, -j, r])
           if (abs(S[j, mm, r]) <= n * lambda * (pi[r]^gamma)) {
             phi[j, mm, r] <- 0
           } else if (S[j, mm, r] > n * lambda * (pi[r]^gamma)) {
@@ -155,18 +156,22 @@ EMGLLF <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda,
 
     # Precompute det(rho[,,r]) for r in 1...k
     detRho <- sapply(1:k, function(r) det(rho[, , r]))
+    sumLogLLH <- 0
     for (i in 1:n)
     {
-      # Update gam[,]
-      for (r in 1:k)
-      {
-        gam[i, r] <- pi[r] * exp(-0.5
-          * sum((Y[i, ] %*% rho[, , r] - X[i, ] %*% phi[, , r])^2)) * detRho[r]
-      }
+      # Update gam[,]; use log to avoid numerical problems
+      logGam <- sapply(1:k, function(r) {
+        log(pi[r]) + log(detRho[r]) - 0.5 *
+          sum((Y[i, ] %*% rho[, , r] - X[i, ] %*% phi[, , r])^2)
+      })
+
+      logGam <- logGam - max(logGam) #adjust without changing proportions
+      gam[i, ] <- exp(logGam)
+      norm_fact <- sum(gam[i, ])
+      gam[i, ] <- gam[i, ] / norm_fact
+      sumLogLLH <- sumLogLLH + log(norm_fact) - log((2 * base::pi)^(m/2))
     }
-    norm_fact <- rowSums(gam)
-    gam <- gam / norm_fact
-    sumLogLLH <- sum(log(norm_fact) - log((2 * base::pi)^(m/2)))
+
     sumPen <- sum(pi^gamma * b)
     last_llh <- llh
     llh <- -sumLogLLH/n + lambda * sumPen

diff --git a/pkg/R/computeGridLambda.R b/pkg/R/computeGridLambda.R
index c34c707..c2e9c8c 100644 (file)
--- a/pkg/R/computeGridLambda.R
+++ b/pkg/R/computeGridLambda.R
@@ -17,7 +17,7 @@
 #'
 #' @export
 computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, 
-  maxi, tau, fast = TRUE)
+  maxi, tau, fast)
 {
   n <- nrow(X)
   p <- dim(phiInit)[1]

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index 8f93fb8..90d0a2a 100644 (file)
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -21,7 +21,7 @@
 #'
 #' @export
 constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini, 
-  maxi, gamma, X, Y, eps, S, ncores = 3, fast = TRUE, verbose = FALSE)
+  maxi, gamma, X, Y, eps, S, ncores = 3, fast, verbose)
 {
   if (ncores > 1)
   {
@@ -51,8 +51,8 @@ constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini,
       return(NULL)
 
     # lambda == 0 because we compute the EMV: no penalization here
-    res <- EMGLLF(phiInit[col.sel, , ], rhoInit, piInit, gamInit, mini, maxi, 
-      gamma, 0, X[, col.sel], Y, eps, fast)
+    res <- EMGLLF(array(phiInit[col.sel, , ],dim=c(length(col.sel),m,k)), rhoInit,
+      piInit, gamInit, mini, maxi, gamma, 0, as.matrix(X[, col.sel]), Y, eps, fast)
 
     # Eval dimension from the result + selected
     phiLambda2 <- res$phi

diff --git a/pkg/R/constructionModelesLassoRank.R b/pkg/R/constructionModelesLassoRank.R
index 5857a42..85685e9 100644 (file)
--- a/pkg/R/constructionModelesLassoRank.R
+++ b/pkg/R/constructionModelesLassoRank.R
@@ -19,7 +19,7 @@
 #'
 #' @export
 constructionModelesLassoRank <- function(S, k, mini, maxi, X, Y, eps, rank.min, rank.max, 
-  ncores, fast = TRUE, verbose = FALSE)
+  ncores, fast, verbose)
 {
   n <- dim(X)[1]
   p <- dim(X)[2]

diff --git a/pkg/R/initSmallEM.R b/pkg/R/initSmallEM.R
index ba95586..056d7e7 100644 (file)
--- a/pkg/R/initSmallEM.R
+++ b/pkg/R/initSmallEM.R
@@ -8,7 +8,7 @@
 #' @export
 #' @importFrom methods new
 #' @importFrom stats cutree dist hclust runif
-initSmallEM <- function(k, X, Y, fast = TRUE)
+initSmallEM <- function(k, X, Y, fast)
 {
   n <- nrow(Y)
   m <- ncol(Y)
@@ -67,8 +67,7 @@ initSmallEM <- function(k, X, Y, fast = TRUE)
     init_EMG <- EMGLLF(phiInit1[, , , repet], rhoInit1[, , , repet], piInit1[repet, ],
       gamInit1[, , repet], miniInit, maxiInit, gamma = 1, lambda = 0, X, Y,
       eps = 1e-04, fast)
-    LLFEessai <- init_EMG$LLF
-    LLFinit1[repet] <- LLFEessai[length(LLFEessai)]
+    LLFinit1[[repet]] <- init_EMG$llh
   }
   b <- which.min(LLFinit1)
   phiInit <- phiInit1[, , , b]

diff --git a/pkg/R/main.R b/pkg/R/main.R
index 64e0586..fecf519 100644 (file)
--- a/pkg/R/main.R
+++ b/pkg/R/main.R
@@ -57,7 +57,7 @@ valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mi
     # smallEM initializes parameters by k-means and regression model in each
     # component, doing this 20 times, and keeping the values maximizing the
     # likelihood after 10 iterations of the EM algorithm.
-    P <- initSmallEM(k, X, Y)
+    P <- initSmallEM(k, X, Y, fast)
     grid_lambda <- computeGridLambda(P$phiInit, P$rhoInit, P$piInit, P$gamInit, 
       X, Y, gamma, mini, maxi, eps, fast)
     if (length(grid_lambda) > size_coll_mod) 

diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R
index f717cae..bfe4042 100644 (file)
--- a/pkg/R/selectVariables.R
+++ b/pkg/R/selectVariables.R
@@ -23,7 +23,7 @@
 #' @export
 #'
 selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, 
-  glambda, X, Y, thresh = 1e-08, eps, ncores = 3, fast = TRUE)
+  glambda, X, Y, thresh = 1e-08, eps, ncores = 3, fast)
 {
   if (ncores > 1) {
     cl <- parallel::makeCluster(ncores, outfile = "")
@@ -52,14 +52,18 @@ selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma
   }
 
   # For each lambda in the grid, we compute the coefficients
-  out <- if (ncores > 1) 
-    parLapply(cl, glambda, computeCoefs) else lapply(glambda, computeCoefs)
+  out <-
+    if (ncores > 1) {
+      parLapply(cl, glambda, computeCoefs)
+    } else {
+      lapply(glambda, computeCoefs)
+    }
   if (ncores > 1) 
     parallel::stopCluster(cl)
   # Suppress models which are computed twice En fait, ca ca fait la comparaison de
   # tous les parametres On veut juste supprimer ceux qui ont les memes variables
-  # sélectionnées sha1_array <- lapply(out, digest::sha1) out[
-  # duplicated(sha1_array) ]
+  # sélectionnées
+  # sha1_array <- lapply(out, digest::sha1) out[ duplicated(sha1_array) ]
   selec <- lapply(out, function(model) model$selected)
   ind_dup <- duplicated(selec)
   ind_uniq <- which(!ind_dup)